Abstract
This paper introduces a fuzzy-extension of the Kohonen Self Organizing Map model called Fuzzy Growing Hierarchical SOM that is able to extract Fuzzy rules in hierarchical way. The main idea of the FGHSOM is to provide an architecture that can be initialized with prior knowledge and without, and can be trained directly using SOM learning methods. The training is carried out using competitive methods in such a way that the learning result is interpretable in the form of linguistic fuzzy if-then rules and rules are organized in a tree-like structure. The structure allows increasing the information using parent/child relationships. The FGHSOM is successfully compared with different neuro-fuzzy algorithms in different classification problems.
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del-Hoyo, R., Medrano, N., Martín-del-Brio, B., Lacueva-Pérez, F.J. (2008). Supervised Classification Fuzzy Growing Hierarchical SOM. In: Corchado, E., Abraham, A., Pedrycz, W. (eds) Hybrid Artificial Intelligence Systems. HAIS 2008. Lecture Notes in Computer Science(), vol 5271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87656-4_28
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DOI: https://doi.org/10.1007/978-3-540-87656-4_28
Publisher Name: Springer, Berlin, Heidelberg
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